A 12.9 fA/rtHz Power-Efficient High-Dynamic-Range Current Front-End for Light-to-Digital Conversion
Bibliographic record
Abstract
Light-to-digital converters are critical in bio-signal acquisition systems such as photoplethysmography (PPG) and fluorescence sensors. These applications demand converters with low input-referred noise (IRN) and high dynamic range (DR) to detect low-level signals from sensing targets in the presence of large varying background levels. Typically, noise performance is limited by the operational transconductance amplifier (OTA) in the capacitive transimpedance amplifier (CTIA) and kT/C noise sampled on the feedback capacitor. kT/C noise is commonly canceled through correlated double sampling, which suffers from noise folding of the high frequency OTA noise. This work presents a four-channel light-to-digital converter IC that utilizes multisample line fitting to suppress kT/C noise and noise folding. A power-efficient gain-boosted folded-cascode OTA topology is employed within the CTIA to further reduce the dominant noise during integration. Compared to recent light-to-digital converter designs, the fabricated IC achieves the lowest IRN of$12.9 \text{fA} / \text{rtHz}$, the best power efficiency of$0.646 \text{fA}^{2} \cdot ~\mathrm{W} / \text{Hz}$, and a high DR of 119.4 dB.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.018 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".